Evaluation of a robust regression method (RoBoost-PLSR) to predict biochemical variables for agronomic applications: Case study of grape berry maturity monitoring-Reference-Cited by-同舟云学术

Evaluation of a robust regression method (RoBoost-PLSR) to predict biochemical variables for agronomic applications: Case study of grape berry maturity monitoring

Published:2022-02 Issue: Volume:221 Page:104485
ISSN:0169-7439
Container-title:Chemometrics and Intelligent Laboratory Systems
language:en
Short-container-title:Chemometrics and Intelligent Laboratory Systems

Author:

Courand Aldrig,Metz Maxime,Héran Daphné,Feilhes Carole,Prezman Fanny,Serrano Eric,Bendoula Ryad,Ryckewaert Maxime^ORCID

Publisher

Elsevier BV

Subject

Spectroscopy,Process Chemistry and Technology,Computer Science Applications,Software,Analytical Chemistry

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3. Massive spectral data analysis for plant breeding using parSketch-PLSDA method: discrimination of sunflower genotypes;Ryckewaert;Biosyst. Eng.,2021

4. Partial robust M-regression;Serneels;Chemometr. Intell. Lab. Syst.,2005

5. Robust methods for partial least squares regression;Hubert;J. Chemometr.,2003

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